In RohanAlexander/AustralianElections: Provides Datasets About Australian Elections
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>"
)
Standard use
Load the package with:
library(AustralianElections)
The purpose of AustralianElections is to make it easier to access data about Australian federal elections. This is done through the use of functions that get and manipulate publicly accessible datasets that were constructed for this purpose. These datasets have every politician in the House of Representatives and the Senate between 1901 and 2021. To get started with the package:
Codes to request a dataset
Request codes are passed to the function get_auselection() to download a specific dataset in AustralianElections. But before downloading any dataset the required code must be known. To show which codes correspond to a given dataset, we can pass the string "codes" to get_auselection(). This will print the needed request codes to the console as a tibble, as seen below. Included in the printout is additional information on each dataset such as the years covered, number of obersvations, and the number of variables.
get_auselection("codes")
# A tibble: 5 x 2
request_code dataset
<chr> <chr>
1 byelections Byelection data - 1901-2018 - 158 obs. - 9 var.
2 elections Election data - 1901-2019 - 46 obs. - 4 var.
3 parliaments Parliament data - 1901-2019 - 75 obs. - 9 var.
4 voting_data Voting data - 1901-2019 - 65337 obs. - 25 var.
5 voting_data_with_ids Voting data with IDs - 1901-2019 - 65337 obs. - 22 var.
Download a dataset
The get_auselection() function can be used in several ways to download a dataset. First, it can be used to access an entire dataset by passing only the associated request code. Doing so will download the requested dataset with all election years to an assigned name. Below we have done this for the voting_data dataset.
# Return the complete voting_data dataset
voting_data_df <- get_auselection("voting_data")
head(voting_data_df)
# A tibble: 6 x 25
division state division_num_enrolled division_num_voted division_percen~ original_name party
<chr> <chr> <dbl> <dbl> <dbl> <chr> <chr>
1 BARRIER NSW 10290 5493 53.4 Benjamin Long NA
2 BARRIER NSW 10290 5493 53.4 Josiah Thomas Lab
3 BLAND NSW 10996 7370 67 Chris Watson Lab
4 BLAND NSW 10996 7370 67 Patrick Heff~ NA
5 BLAND NSW 10996 7370 67 William Lucas FT
6 CANOBOLAS NSW 11025 7717 70 Bernhard Wise Prot
# ... with 18 more variables: vote_redistribution_round <dbl>, votes_count <dbl>,
# votes_share <dbl>, swing <dbl>, redistributed_vote_count <dbl>,
# redistributed_vote_share <dbl>, txt_file <chr>, election_date <date>, starred <lgl>,
# plus <lgl>, surname <chr>, capitalised <lgl>, full_name <chr>, division_sent <chr>,
# full_name_for_matching <chr>, twoPP <dbl>, number_of_candidates <dbl>, winnerDummy <dbl>
Second, the function can be used to filter for only a specific year. This can be done as follows:
# Return the voting_data dataset for only the year 2018
voting_data_2018_df <- get_auselection("voting_data", year = 2018)
head(voting_data_2018_df)
# A tibble: 6 x 25
division state division_num_enrolled division_num_voted division_percent~ original_name party
<chr> <chr> <dbl> <dbl> <dbl> <chr> <chr>
1 BATMAN VIC 111857 91054 81.4 Adrian White~ NA
2 BATMAN VIC 111857 91054 81.4 Alex Bhathal Grn
3 BATMAN VIC 111857 91054 81.4 Debbie Robin~ NA
4 BATMAN VIC 111857 91054 81.4 Ged Kearney ALP
5 BATMAN VIC 111857 91054 81.4 Kevin Bailey ACon
6 BATMAN VIC 111857 91054 81.4 Mark Mcdonald NA
# ... with 18 more variables: vote_redistribution_round <dbl>, votes_count <dbl>,
# votes_share <dbl>, swing <dbl>, redistributed_vote_count <dbl>,
# redistributed_vote_share <dbl>, txt_file <chr>, election_date <date>, starred <lgl>,
# plus <lgl>, surname <chr>, capitalised <lgl>, full_name <chr>, division_sent <chr>,
# full_name_for_matching <chr>, twoPP <dbl>, number_of_candidates <dbl>, winnerDummy <dbl>
If you are unsure of in which years an election was held, you can use the function call get_auselection("years") to return a vector of election years.
# Show in which years an election was held
get_auselection("years")
[1] 1901 1903 1906 1910 1913 1914 1917 1919 1922 1925 1928 1929 1931 1934 1937 1940
[17] 1943 1946 1949 1951 1954 1955 1958 1961 1963 1966 1969 1972 1974 1975 1977 1980
[33] 1983 1984 1987 1990 1993 1996 1998 2001 2004 2007 2010 2013 2016 2019
Third, the function can also be used to filter for a range of years. For instance, elections years greater or less than 1975; every election year but 1975; or election years from 1975 to 2019. This can be done by including a relational operator (>, <, >=, <=, or !=) or the string "range" as follows:
# Return the voting_data dataset for years greater than or equal to 1975
voting_data_gte_1975 <- get_auselection("voting_data", opr = ">=", year = 1975)
head(voting_data_gte_1975)
# A tibble: 6 x 25
division state division_num_enrolled division_num_voted division_percent~ original_name party
<chr> <chr> <dbl> <dbl> <dbl> <chr> <chr>
1 BASS TAS 46744 43563 93.2 John Macrost~ ALP
2 BASS TAS 46744 43563 93.2 Kevin Newman Lib
3 BASS TAS 46744 43563 93.2 Marcus Aussi~ NA
4 BASS TAS 46744 43563 93.2 Paul Kent NA
5 BASS TAS 46744 43563 93.2 Sydney Negus NA
6 BASS TAS 46744 43563 93.2 Violet Petro~ NA
# ... with 18 more variables: vote_redistribution_round <dbl>, votes_count <dbl>,
# votes_share <dbl>, swing <dbl>, redistributed_vote_count <dbl>,
# redistributed_vote_share <dbl>, txt_file <chr>, election_date <date>, starred <lgl>,
# plus <lgl>, surname <chr>, capitalised <lgl>, full_name <chr>, division_sent <chr>,
# full_name_for_matching <chr>, twoPP <dbl>, number_of_candidates <dbl>, winnerDummy <dbl>
# Return every year but 1975 for voting_data dataset
voting_data_not_75 <- get_auselection("voting_data", opr = "!=", year = 1975)
head(voting_data_not_75)
# A tibble: 6 x 25
division state division_num_enrolled division_num_voted division_percen~ original_name party
<chr> <chr> <dbl> <dbl> <dbl> <chr> <chr>
1 BARRIER NSW 10290 5493 53.4 Benjamin Long NA
2 BARRIER NSW 10290 5493 53.4 Josiah Thomas Lab
3 BLAND NSW 10996 7370 67 Chris Watson Lab
4 BLAND NSW 10996 7370 67 Patrick Heff~ NA
5 BLAND NSW 10996 7370 67 William Lucas FT
6 CANOBOLAS NSW 11025 7717 70 Bernhard Wise Prot
# ... with 18 more variables: vote_redistribution_round <dbl>, votes_count <dbl>,
# votes_share <dbl>, swing <dbl>, redistributed_vote_count <dbl>,
# redistributed_vote_share <dbl>, txt_file <chr>, election_date <date>, starred <lgl>,
# plus <lgl>, surname <chr>, capitalised <lgl>, full_name <chr>, division_sent <chr>,
# full_name_for_matching <chr>, twoPP <dbl>, number_of_candidates <dbl>, winnerDummy <dbl>
In the case of requesting a range of election years, we use the string "range" in place of the relational operator. This allows the passing of a vector of years to year. It does not matter which order the years are given in the vector as the function only reads the max and min values.
# Return the voting_data dataset for elections from 1975 to 2019
voting_data_7519 <- get_auselection("voting_data", opr = "range", year = c(1975, 2019))
head(voting_data_7519)
# A tibble: 6 x 25
division state division_num_enrolled division_num_voted division_percent~ original_name party
<chr> <chr> <dbl> <dbl> <dbl> <chr> <chr>
1 BASS TAS 46744 43563 93.2 John Macrost~ ALP
2 BASS TAS 46744 43563 93.2 Kevin Newman Lib
3 BASS TAS 46744 43563 93.2 Marcus Aussi~ NA
4 BASS TAS 46744 43563 93.2 Paul Kent NA
5 BASS TAS 46744 43563 93.2 Sydney Negus NA
6 BASS TAS 46744 43563 93.2 Violet Petro~ NA
# ... with 18 more variables: vote_redistribution_round <dbl>, votes_count <dbl>,
# votes_share <dbl>, swing <dbl>, redistributed_vote_count <dbl>,
# redistributed_vote_share <dbl>, txt_file <chr>, election_date <date>, starred <lgl>,
# plus <lgl>, surname <chr>, capitalised <lgl>, full_name <chr>, division_sent <chr>,
# full_name_for_matching <chr>, twoPP <dbl>, number_of_candidates <dbl>, winnerDummy <dbl>
# Return the voting_data dataset for elections from 1975 to 2019 reversing the requested years
# This returns the exact same dataset as the one above
voting_data_1975 <- get_auselection("voting_data", opr = "range", year = c(2019, 1975))
head(voting_data_1975)
# A tibble: 6 x 25
division state division_num_enrolled division_num_voted division_percent~ original_name party
<chr> <chr> <dbl> <dbl> <dbl> <chr> <chr>
1 BASS TAS 46744 43563 93.2 John Macrost~ ALP
2 BASS TAS 46744 43563 93.2 Kevin Newman Lib
3 BASS TAS 46744 43563 93.2 Marcus Aussi~ NA
4 BASS TAS 46744 43563 93.2 Paul Kent NA
5 BASS TAS 46744 43563 93.2 Sydney Negus NA
6 BASS TAS 46744 43563 93.2 Violet Petro~ NA
# ... with 18 more variables: vote_redistribution_round <dbl>, votes_count <dbl>,
# votes_share <dbl>, swing <dbl>, redistributed_vote_count <dbl>,
# redistributed_vote_share <dbl>, txt_file <chr>, election_date <date>, starred <lgl>,
# plus <lgl>, surname <chr>, capitalised <lgl>, full_name <chr>, division_sent <chr>,
# full_name_for_matching <chr>, twoPP <dbl>, number_of_candidates <dbl>, winnerDummy <dbl>
Two-party-preferred and Winners
Along with complete datasets and the ability to filter by election year, AustralianElections also provides access to two pre-filtered datasets. These datasets are filtered by either the two-party-preferred results or the election winners.
Two-party-preferred
We can use the auselect_twopp() function to access the pre-filtered two-party-preferred dataset. This provides access to a filtered version of either the voting_data_with_ids dataset or voting_data dataset. By default, the voting_data_with_ids dataset is returned.
twopp_ids <- auselect_twopp()
head(twopp_ids)
# A tibble: 6 x 22
division state division_num_enrolled division_num_vo~ division_percen~ original_name party
<chr> <chr> <dbl> <dbl> <dbl> <chr> <chr>
1 CORANGAMITE VIC 34895 25553 73.2 Scullin ALP
2 CORANGAMITE VIC 34895 25553 73.2 Gibson VFU
3 ECHUCA VIC 34704 20638 59.5 Edwin Purbri~ NA
4 ECHUCA VIC 34704 20638 59.5 Frederick Pu~ Nat
5 ECHUCA VIC 34704 20638 59.5 William Hill VFU
6 BARRIER NSW 26825 17634 65.7 Arthur Lawre~ NA
# ... with 15 more variables: vote_redistribution_round <dbl>, votes_count <dbl>,
# votes_share <dbl>, swing <dbl>, redistributed_vote_count <dbl>,
# redistributed_vote_share <dbl>, txt_file <chr>, election_date <date>, starred <lgl>,
# plus <lgl>, capitalised <lgl>, twoPP <dbl>, number_of_candidates <dbl>, winnerDummy <dbl>,
# uniqueID <chr>
To request the voting_data version we can include the argument w_ids = FALSE in the function.
twopp_noids <- auselect_twopp(w_ids = FALSE)
head(twopp_noids)
# A tibble: 6 x 25
division state division_num_enrolled division_num_vo~ division_percen~ original_name party
<chr> <chr> <dbl> <dbl> <dbl> <chr> <chr>
1 CORANGAMITE VIC 34895 25553 73.2 Scullin ALP
2 CORANGAMITE VIC 34895 25553 73.2 Gibson VFU
3 ECHUCA VIC 34704 20638 59.5 Edwin Purbri~ NA
4 ECHUCA VIC 34704 20638 59.5 Frederick Pu~ Nat
5 ECHUCA VIC 34704 20638 59.5 William Hill VFU
6 BARRIER NSW 26825 17634 65.7 Arthur Lawre~ NA
# ... with 18 more variables: vote_redistribution_round <dbl>, votes_count <dbl>,
# votes_share <dbl>, swing <dbl>, redistributed_vote_count <dbl>,
# redistributed_vote_share <dbl>, txt_file <chr>, election_date <date>, starred <lgl>,
# plus <lgl>, surname <chr>, capitalised <lgl>, full_name <chr>, division_sent <chr>,
# full_name_for_matching <chr>, twoPP <dbl>, number_of_candidates <dbl>, winnerDummy <dbl>
Winners
To access the pre-filtered election winners dataset we can use the auselect_winners() function. This works similarly to the auselect_twopp() function in that it provides access to either the voting_data_with_ids dataset or voting_data dataset. Again, by default the voting_data_with_ids dataset is returned, and to return the voting_data dataset the argument w_ids = FALSE need be passed to the function.
winners_ids <- auselect_winners()
head(winners_ids)
# A tibble: 6 x 22
division state division_num_enrolled division_num_voted division_percen~ original_name party
<chr> <chr> <dbl> <dbl> <dbl> <chr> <chr>
1 BARRIER NSW 10290 5493 53.4 Josiah Thomas Lab
2 BLAND NSW 10996 7370 67 Chris Watson Lab
3 CANOBOLAS NSW 11025 7717 70 Thomas Brown Lab
4 COWPER NSW 12802 8632 67.4 Francis Clar~ Prot
5 DALLEY NSW 15110 10964 72.6 William Wilks FT
6 DARLING NSW 8930 5092 57 William Spen~ Lab
# ... with 15 more variables: vote_redistribution_round <dbl>, votes_count <dbl>,
# votes_share <dbl>, swing <dbl>, redistributed_vote_count <dbl>,
# redistributed_vote_share <dbl>, txt_file <chr>, election_date <date>, starred <lgl>,
# plus <lgl>, capitalised <lgl>, twoPP <dbl>, number_of_candidates <dbl>, winnerDummy <dbl>,
# uniqueID <chr>
winners_noids <- auselect_winners(w_ids = FALSE)
head(winners_noids)
# A tibble: 6 x 25
division state division_num_enrolled division_num_voted division_percen~ original_name party
<chr> <chr> <dbl> <dbl> <dbl> <chr> <chr>
1 BARRIER NSW 10290 5493 53.4 Josiah Thomas Lab
2 BLAND NSW 10996 7370 67 Chris Watson Lab
3 CANOBOLAS NSW 11025 7717 70 Thomas Brown Lab
4 COWPER NSW 12802 8632 67.4 Francis Clar~ Prot
5 DALLEY NSW 15110 10964 72.6 William Wilks FT
6 DARLING NSW 8930 5092 57 William Spen~ Lab
# ... with 18 more variables: vote_redistribution_round <dbl>, votes_count <dbl>,
# votes_share <dbl>, swing <dbl>, redistributed_vote_count <dbl>,
# redistributed_vote_share <dbl>, txt_file <chr>, election_date <date>, starred <lgl>,
# plus <lgl>, surname <chr>, capitalised <lgl>, full_name <chr>, division_sent <chr>,
# full_name_for_matching <chr>, twoPP <dbl>, number_of_candidates <dbl>, winnerDummy <dbl>
RohanAlexander/AustralianElections documentation built on Dec. 18, 2021, 10:59 a.m.
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knitr::opts_chunk$set( collapse = TRUE, comment = "#>" )
Standard use
Load the package with:
library(AustralianElections)
The purpose of AustralianElections is to make it easier to access data about Australian federal elections. This is done through the use of functions that get and manipulate publicly accessible datasets that were constructed for this purpose. These datasets have every politician in the House of Representatives and the Senate between 1901 and 2021. To get started with the package:
Codes to request a dataset
Request codes are passed to the function get_auselection() to download a specific dataset in AustralianElections. But before downloading any dataset the required code must be known. To show which codes correspond to a given dataset, we can pass the string "codes" to get_auselection(). This will print the needed request codes to the console as a tibble, as seen below. Included in the printout is additional information on each dataset such as the years covered, number of obersvations, and the number of variables.
get_auselection("codes") # A tibble: 5 x 2 request_code dataset <chr> <chr> 1 byelections Byelection data - 1901-2018 - 158 obs. - 9 var. 2 elections Election data - 1901-2019 - 46 obs. - 4 var. 3 parliaments Parliament data - 1901-2019 - 75 obs. - 9 var. 4 voting_data Voting data - 1901-2019 - 65337 obs. - 25 var. 5 voting_data_with_ids Voting data with IDs - 1901-2019 - 65337 obs. - 22 var.
Download a dataset
The get_auselection() function can be used in several ways to download a dataset. First, it can be used to access an entire dataset by passing only the associated request code. Doing so will download the requested dataset with all election years to an assigned name. Below we have done this for the voting_data dataset.
# Return the complete voting_data dataset voting_data_df <- get_auselection("voting_data") head(voting_data_df) # A tibble: 6 x 25 division state division_num_enrolled division_num_voted division_percen~ original_name party <chr> <chr> <dbl> <dbl> <dbl> <chr> <chr> 1 BARRIER NSW 10290 5493 53.4 Benjamin Long NA 2 BARRIER NSW 10290 5493 53.4 Josiah Thomas Lab 3 BLAND NSW 10996 7370 67 Chris Watson Lab 4 BLAND NSW 10996 7370 67 Patrick Heff~ NA 5 BLAND NSW 10996 7370 67 William Lucas FT 6 CANOBOLAS NSW 11025 7717 70 Bernhard Wise Prot # ... with 18 more variables: vote_redistribution_round <dbl>, votes_count <dbl>, # votes_share <dbl>, swing <dbl>, redistributed_vote_count <dbl>, # redistributed_vote_share <dbl>, txt_file <chr>, election_date <date>, starred <lgl>, # plus <lgl>, surname <chr>, capitalised <lgl>, full_name <chr>, division_sent <chr>, # full_name_for_matching <chr>, twoPP <dbl>, number_of_candidates <dbl>, winnerDummy <dbl>
Second, the function can be used to filter for only a specific year. This can be done as follows:
# Return the voting_data dataset for only the year 2018 voting_data_2018_df <- get_auselection("voting_data", year = 2018) head(voting_data_2018_df) # A tibble: 6 x 25 division state division_num_enrolled division_num_voted division_percent~ original_name party <chr> <chr> <dbl> <dbl> <dbl> <chr> <chr> 1 BATMAN VIC 111857 91054 81.4 Adrian White~ NA 2 BATMAN VIC 111857 91054 81.4 Alex Bhathal Grn 3 BATMAN VIC 111857 91054 81.4 Debbie Robin~ NA 4 BATMAN VIC 111857 91054 81.4 Ged Kearney ALP 5 BATMAN VIC 111857 91054 81.4 Kevin Bailey ACon 6 BATMAN VIC 111857 91054 81.4 Mark Mcdonald NA # ... with 18 more variables: vote_redistribution_round <dbl>, votes_count <dbl>, # votes_share <dbl>, swing <dbl>, redistributed_vote_count <dbl>, # redistributed_vote_share <dbl>, txt_file <chr>, election_date <date>, starred <lgl>, # plus <lgl>, surname <chr>, capitalised <lgl>, full_name <chr>, division_sent <chr>, # full_name_for_matching <chr>, twoPP <dbl>, number_of_candidates <dbl>, winnerDummy <dbl>
If you are unsure of in which years an election was held, you can use the function call get_auselection("years") to return a vector of election years.
# Show in which years an election was held get_auselection("years") [1] 1901 1903 1906 1910 1913 1914 1917 1919 1922 1925 1928 1929 1931 1934 1937 1940 [17] 1943 1946 1949 1951 1954 1955 1958 1961 1963 1966 1969 1972 1974 1975 1977 1980 [33] 1983 1984 1987 1990 1993 1996 1998 2001 2004 2007 2010 2013 2016 2019
Third, the function can also be used to filter for a range of years. For instance, elections years greater or less than 1975; every election year but 1975; or election years from 1975 to 2019. This can be done by including a relational operator (>, <, >=, <=, or !=) or the string "range" as follows:
# Return the voting_data dataset for years greater than or equal to 1975 voting_data_gte_1975 <- get_auselection("voting_data", opr = ">=", year = 1975) head(voting_data_gte_1975) # A tibble: 6 x 25 division state division_num_enrolled division_num_voted division_percent~ original_name party <chr> <chr> <dbl> <dbl> <dbl> <chr> <chr> 1 BASS TAS 46744 43563 93.2 John Macrost~ ALP 2 BASS TAS 46744 43563 93.2 Kevin Newman Lib 3 BASS TAS 46744 43563 93.2 Marcus Aussi~ NA 4 BASS TAS 46744 43563 93.2 Paul Kent NA 5 BASS TAS 46744 43563 93.2 Sydney Negus NA 6 BASS TAS 46744 43563 93.2 Violet Petro~ NA # ... with 18 more variables: vote_redistribution_round <dbl>, votes_count <dbl>, # votes_share <dbl>, swing <dbl>, redistributed_vote_count <dbl>, # redistributed_vote_share <dbl>, txt_file <chr>, election_date <date>, starred <lgl>, # plus <lgl>, surname <chr>, capitalised <lgl>, full_name <chr>, division_sent <chr>, # full_name_for_matching <chr>, twoPP <dbl>, number_of_candidates <dbl>, winnerDummy <dbl>
# Return every year but 1975 for voting_data dataset voting_data_not_75 <- get_auselection("voting_data", opr = "!=", year = 1975) head(voting_data_not_75) # A tibble: 6 x 25 division state division_num_enrolled division_num_voted division_percen~ original_name party <chr> <chr> <dbl> <dbl> <dbl> <chr> <chr> 1 BARRIER NSW 10290 5493 53.4 Benjamin Long NA 2 BARRIER NSW 10290 5493 53.4 Josiah Thomas Lab 3 BLAND NSW 10996 7370 67 Chris Watson Lab 4 BLAND NSW 10996 7370 67 Patrick Heff~ NA 5 BLAND NSW 10996 7370 67 William Lucas FT 6 CANOBOLAS NSW 11025 7717 70 Bernhard Wise Prot # ... with 18 more variables: vote_redistribution_round <dbl>, votes_count <dbl>, # votes_share <dbl>, swing <dbl>, redistributed_vote_count <dbl>, # redistributed_vote_share <dbl>, txt_file <chr>, election_date <date>, starred <lgl>, # plus <lgl>, surname <chr>, capitalised <lgl>, full_name <chr>, division_sent <chr>, # full_name_for_matching <chr>, twoPP <dbl>, number_of_candidates <dbl>, winnerDummy <dbl>
In the case of requesting a range of election years, we use the string "range" in place of the relational operator. This allows the passing of a vector of years to year. It does not matter which order the years are given in the vector as the function only reads the max and min values.
# Return the voting_data dataset for elections from 1975 to 2019 voting_data_7519 <- get_auselection("voting_data", opr = "range", year = c(1975, 2019)) head(voting_data_7519) # A tibble: 6 x 25 division state division_num_enrolled division_num_voted division_percent~ original_name party <chr> <chr> <dbl> <dbl> <dbl> <chr> <chr> 1 BASS TAS 46744 43563 93.2 John Macrost~ ALP 2 BASS TAS 46744 43563 93.2 Kevin Newman Lib 3 BASS TAS 46744 43563 93.2 Marcus Aussi~ NA 4 BASS TAS 46744 43563 93.2 Paul Kent NA 5 BASS TAS 46744 43563 93.2 Sydney Negus NA 6 BASS TAS 46744 43563 93.2 Violet Petro~ NA # ... with 18 more variables: vote_redistribution_round <dbl>, votes_count <dbl>, # votes_share <dbl>, swing <dbl>, redistributed_vote_count <dbl>, # redistributed_vote_share <dbl>, txt_file <chr>, election_date <date>, starred <lgl>, # plus <lgl>, surname <chr>, capitalised <lgl>, full_name <chr>, division_sent <chr>, # full_name_for_matching <chr>, twoPP <dbl>, number_of_candidates <dbl>, winnerDummy <dbl>
# Return the voting_data dataset for elections from 1975 to 2019 reversing the requested years # This returns the exact same dataset as the one above voting_data_1975 <- get_auselection("voting_data", opr = "range", year = c(2019, 1975)) head(voting_data_1975) # A tibble: 6 x 25 division state division_num_enrolled division_num_voted division_percent~ original_name party <chr> <chr> <dbl> <dbl> <dbl> <chr> <chr> 1 BASS TAS 46744 43563 93.2 John Macrost~ ALP 2 BASS TAS 46744 43563 93.2 Kevin Newman Lib 3 BASS TAS 46744 43563 93.2 Marcus Aussi~ NA 4 BASS TAS 46744 43563 93.2 Paul Kent NA 5 BASS TAS 46744 43563 93.2 Sydney Negus NA 6 BASS TAS 46744 43563 93.2 Violet Petro~ NA # ... with 18 more variables: vote_redistribution_round <dbl>, votes_count <dbl>, # votes_share <dbl>, swing <dbl>, redistributed_vote_count <dbl>, # redistributed_vote_share <dbl>, txt_file <chr>, election_date <date>, starred <lgl>, # plus <lgl>, surname <chr>, capitalised <lgl>, full_name <chr>, division_sent <chr>, # full_name_for_matching <chr>, twoPP <dbl>, number_of_candidates <dbl>, winnerDummy <dbl>
Two-party-preferred and Winners
Along with complete datasets and the ability to filter by election year, AustralianElections also provides access to two pre-filtered datasets. These datasets are filtered by either the two-party-preferred results or the election winners.
Two-party-preferred
We can use the auselect_twopp() function to access the pre-filtered two-party-preferred dataset. This provides access to a filtered version of either the voting_data_with_ids dataset or voting_data dataset. By default, the voting_data_with_ids dataset is returned.
twopp_ids <- auselect_twopp() head(twopp_ids) # A tibble: 6 x 22 division state division_num_enrolled division_num_vo~ division_percen~ original_name party <chr> <chr> <dbl> <dbl> <dbl> <chr> <chr> 1 CORANGAMITE VIC 34895 25553 73.2 Scullin ALP 2 CORANGAMITE VIC 34895 25553 73.2 Gibson VFU 3 ECHUCA VIC 34704 20638 59.5 Edwin Purbri~ NA 4 ECHUCA VIC 34704 20638 59.5 Frederick Pu~ Nat 5 ECHUCA VIC 34704 20638 59.5 William Hill VFU 6 BARRIER NSW 26825 17634 65.7 Arthur Lawre~ NA # ... with 15 more variables: vote_redistribution_round <dbl>, votes_count <dbl>, # votes_share <dbl>, swing <dbl>, redistributed_vote_count <dbl>, # redistributed_vote_share <dbl>, txt_file <chr>, election_date <date>, starred <lgl>, # plus <lgl>, capitalised <lgl>, twoPP <dbl>, number_of_candidates <dbl>, winnerDummy <dbl>, # uniqueID <chr>
To request the voting_data version we can include the argument w_ids = FALSE in the function.
twopp_noids <- auselect_twopp(w_ids = FALSE) head(twopp_noids) # A tibble: 6 x 25 division state division_num_enrolled division_num_vo~ division_percen~ original_name party <chr> <chr> <dbl> <dbl> <dbl> <chr> <chr> 1 CORANGAMITE VIC 34895 25553 73.2 Scullin ALP 2 CORANGAMITE VIC 34895 25553 73.2 Gibson VFU 3 ECHUCA VIC 34704 20638 59.5 Edwin Purbri~ NA 4 ECHUCA VIC 34704 20638 59.5 Frederick Pu~ Nat 5 ECHUCA VIC 34704 20638 59.5 William Hill VFU 6 BARRIER NSW 26825 17634 65.7 Arthur Lawre~ NA # ... with 18 more variables: vote_redistribution_round <dbl>, votes_count <dbl>, # votes_share <dbl>, swing <dbl>, redistributed_vote_count <dbl>, # redistributed_vote_share <dbl>, txt_file <chr>, election_date <date>, starred <lgl>, # plus <lgl>, surname <chr>, capitalised <lgl>, full_name <chr>, division_sent <chr>, # full_name_for_matching <chr>, twoPP <dbl>, number_of_candidates <dbl>, winnerDummy <dbl>
Winners
To access the pre-filtered election winners dataset we can use the auselect_winners() function. This works similarly to the auselect_twopp() function in that it provides access to either the voting_data_with_ids dataset or voting_data dataset. Again, by default the voting_data_with_ids dataset is returned, and to return the voting_data dataset the argument w_ids = FALSE need be passed to the function.
winners_ids <- auselect_winners() head(winners_ids) # A tibble: 6 x 22 division state division_num_enrolled division_num_voted division_percen~ original_name party <chr> <chr> <dbl> <dbl> <dbl> <chr> <chr> 1 BARRIER NSW 10290 5493 53.4 Josiah Thomas Lab 2 BLAND NSW 10996 7370 67 Chris Watson Lab 3 CANOBOLAS NSW 11025 7717 70 Thomas Brown Lab 4 COWPER NSW 12802 8632 67.4 Francis Clar~ Prot 5 DALLEY NSW 15110 10964 72.6 William Wilks FT 6 DARLING NSW 8930 5092 57 William Spen~ Lab # ... with 15 more variables: vote_redistribution_round <dbl>, votes_count <dbl>, # votes_share <dbl>, swing <dbl>, redistributed_vote_count <dbl>, # redistributed_vote_share <dbl>, txt_file <chr>, election_date <date>, starred <lgl>, # plus <lgl>, capitalised <lgl>, twoPP <dbl>, number_of_candidates <dbl>, winnerDummy <dbl>, # uniqueID <chr>
winners_noids <- auselect_winners(w_ids = FALSE) head(winners_noids) # A tibble: 6 x 25 division state division_num_enrolled division_num_voted division_percen~ original_name party <chr> <chr> <dbl> <dbl> <dbl> <chr> <chr> 1 BARRIER NSW 10290 5493 53.4 Josiah Thomas Lab 2 BLAND NSW 10996 7370 67 Chris Watson Lab 3 CANOBOLAS NSW 11025 7717 70 Thomas Brown Lab 4 COWPER NSW 12802 8632 67.4 Francis Clar~ Prot 5 DALLEY NSW 15110 10964 72.6 William Wilks FT 6 DARLING NSW 8930 5092 57 William Spen~ Lab # ... with 18 more variables: vote_redistribution_round <dbl>, votes_count <dbl>, # votes_share <dbl>, swing <dbl>, redistributed_vote_count <dbl>, # redistributed_vote_share <dbl>, txt_file <chr>, election_date <date>, starred <lgl>, # plus <lgl>, surname <chr>, capitalised <lgl>, full_name <chr>, division_sent <chr>, # full_name_for_matching <chr>, twoPP <dbl>, number_of_candidates <dbl>, winnerDummy <dbl>
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